(a) Field of the Invention
The present invention relates to a method and apparatus for processing sensor data in a sensor network. More particularly, the present invention relates to a method and apparatus for processing sensor data that can distinguish and classify feature information of sensor data that is acquired from different kinds of sensors in a sensor network.
(b) Description of the Related Art
In a sensor network-based integration control system such as a ubiquitous sensor network (USN), a wireless sensor network (WSN), and a machine-to-machine network (M2M), in order to provide situation recognition information, sensor data processing technology that can distinguish and classify data that are sensed by a sensor to correspond to a specific situation is very important. Sensor data processing technology for such situation recognition generally uses pattern recognition technology, which is a machine learning technique that teaches a set of sensed data to a machine and that enables the machine to distinguish or classify situation information about newly sensed data.
A sensor data processing process generally using pattern recognition technology includes a pretreatment step of extracting a feature vector by removing noise and analyzing a characteristic pattern in order to well represent a characteristic of sensor data, a step of selecting and modeling an importance feature vector from feature vectors that are formed in multi-dimensions using a dimension reduction technique such as principle component analysis or linear component analysis, and a step of determining situation recognition information by applying a clustering algorithm or classifier algorithm of a feature vector model of newly collected sensor data using supervise learning and unsupervised learning techniques from a modeled feature vector set.
A service providing situation recognition information through such machine learning can provide good situation recognition performance as a dimension of a feature vector is high, but there is a problem that a system memory request amount and a calculation amount of a high dimensional feature vector increase. Particularly, when situation recognition information is provided through cooperation of various sensors, there is a problem that a dimension reduction process of a dynamic feature vector of a large quantity of sensor data should be performed. Further, because extracted feature vectors are classified using machine learning, classification models according to a feature vector group are requested according to a classification purpose, and system complexity thus increases and therefore it is difficult to secure real-time processing.